Staging of idiopathic pulmonary fibrosis: past, present and future
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is traditionally staged with terms such as "mild", "severe", "early" and "advanced" based on pulmonary function tests. This approach allows physicians to monitor disease progression and advise patients and their families. However, it is not known if the stages of this model reflect distinct biological or clinical phenotypes and the therapeutic and prognostic value of this system is limited. Novel methods of IPF staging have recently been developed. The GAP model includes four baseline variables that were found to be predictive of outcome, as identified by logistic regression. These factors are: gender (G), age (A) and two lung physiology variables (P) (forced vital capacity and diffusing capacity of the lung for carbon monoxide). The clinical utility and accuracy of staging models may be further improved in the future by the integration of dynamic parameters that can be measured over time, as well as biological data from biomarkers which may be able to directly measure disease activity. The development of an evidence-based, multidimensional IPF staging model that builds on the current staging approaches to IPF is an important objective for improving the management of IPF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".